Widely known are communication throughput prediction devices that predict a communication throughput, which is a data size (an amount of data) distributed (transmitted) per unit time via a communication network.
Examples of the communication throughput prediction devices include a communication device described in Patent Literature 1. The communication device described in Patent Literature 1 is a server device that distributes video stream data, for example. The server device predicts a communication throughput without requiring feedback from a receiving terminal and distributes video stream data at a rate corresponding to the predicted communication throughput. By using the predicted communication throughput, the server device can prevent reproduction from stopping, reliably distribute the video data, and increase the video quality as much as possible. As described above, prediction of the communication throughput may be used for various purposes.
Non Patent Literature 1 describes an example of a method for predicting a communication throughput used by the communication throughput prediction devices of this type.
The method for predicting a communication throughput described in Non Patent Literature 1 turns fluctuations in the communication throughput into a Brownian motion model with drift. The method then estimates the drift and dispersion serving as parameters of the obtained model from time-series data of the communication throughput for a certain past period. Because Non Patent Literature 1 refers to the estimation as a term “identification”, the description below also uses the term “identification” in the same meaning.
The method for predicting a communication throughput described in Non Patent Literature 1 then calculates probability distribution (probability density function) of a prospective communication throughput based on the identified model, thereby calculating stochastic spread (stochastic diffusion) of the prospective communication throughput based on the calculated probability density function.